Every company uses or produces data in different amounts, formats, and systems. The data themselves aren’t the main goal – it’s their analysis and usage (such as forecasting trends in a particular industry) that allow the company to fully use its business potential. Learn how to use the potential of your data, even if their amounts or variety of formats feel overwhelming.
- Information about website user behaviors generated by stationary Google Analytics tools and Google Analytics mobile application
- Data from different advertisement platforms, such as Facebook Ads and Google Ads
- Data from different databases, such as MySQL and PostgreSQL
- Data in formats JSON and XML
The list goes on. The more data we connect, the broader the context we get. Thanks to this, the decision can be made based on the real numbers and therefore they will be much more correct
Which business fields require data integration?
Big Data
Managing huge amounts of diverse data is a real challenge. Data amount isn’t the hardest part; it’s their complexity, structure, and models. Thanks to the integration, it’s easier for Big Data to understand all information, whether they are generated within the organization, or on the outside.
Data Silos
Silos are data from different sources stored in particular locations. If some parts of data are places in older systems, connecting them to the newer silos might pose a problem. Data integration can help to capture data older, outdated data in the newer systems that every team member can easily access.
Semantic integration
If you ever “cleaned” a simple Excel sheet, you are familiar with many types of data that describe the same aspect but are organized differently. For example, there are several ways of storing dates: “DD/MM/YYYY”, “MM/DD/YYYY”, “Month, Day, Year”, and so on. When you delete the differences and create a decluttered data warehouse, it’s easier to find data as well as analyze and make sense of patterns.
Availability
When you create a central repository, all data users at the company and on the outside have access to the same information. This reduces the number of asked questions as well as incorrect data replication and makes it easier and faster to access data.
The data integration, application integration, and ETL
The data integration is often confused with the application integration and ETL/ELT. These are similar concepts, but they are not the same. There are several significant differences.
Data integration is a process during which data from many sources get together into one centralized location, usually a data warehouse. The end location has to be flexible enough to handle different types of data in large volumes.
The application integration is moving data between particular applications to synchronize them. Usually, each application has a specified way of sending and receiving data and they’re moved around in smaller pieces. An example is integrating a customer service system and an accounting system so that they can operate on the same customer data.
ETL stands for “Extract, Transform, Load”. Those words refer to the three steps in the process: extracting data from source systems, transforming them to a different structure of format, and finally loading them to a target system. Data integration and application integration are two different types of ETL.
Let’s move on to the promised ways to data integration. You know it’s worth it but how do you do it?
5 ways to integrate your data
There are several methods for maintaining data in an integrated form. Learn the 5 ways to data integration. They depend on the company size, business case, and available resources.
- A manual data integration is a process in which a single user manually collects data from different sources through direct access to the interfaces. Then, the user cleans the data according to their needs and connects them into a single warehouse. This can be ineffective and inconsistent but it can work in the smallest organizations that have minimal data resources.
- Middleware data integration is when an application works as an intermediary. It helps to normalize the data and capture them in the main database. The middleware is used when a data integration system cannot access one of the databases on its own.
- Integration based on an application goes further. In this case, the application is not an intermediary as it independently locates, downloads, and integrates data. During the integration, the software’s goal is to make data from different systems compatible and easy to transfer between the sources.
- Consolidated access integration creates an interface that makes the data from different sources seem consistent but keeps them in their original databases. Creating such alleged consistency between databases can be achieved using object systems for database management.
- Common mass memory integration is the usual approach. Data copy is stored in an integrated system and process for a compatible view. It’s the opposite of consolidated integration that keeps the data in their sources. The approach based on common data storage is the fundament of the traditional data warehouse.
It’s very likely that every day, you use more than one of the data integration methods mentioned above. Each one of these methods needs different skills and tools. Is there a solution that would allow you to perform every method we talked about?
One to find them all
You can easily implement each approach with FME, a data integration platform that supports more than 450 data formats. FME was created and is constantly developed by Safe Software, a Canadian company that exists since 1993 and supports more than 10 thousand organizations around the world. In 2020, Safe Software appeared in the Data Integration Tools category of the prestigious Magic Quadrant report from Gartner.
If you want to know more or if you need support with data integration process at your company, reach out to us.
